Health risks of Cryptosporidium and Giardia in the application of surface water and septic tank effluent in Chinese agriculture: Impact on cancer patients identified by quantitative microbial risk assessment.

The protozoa Cryptosporidium and Giardia are major causes of diarrhea and are commonly found on vegetables in China. They pose a health risk, particularly to immunocompromised individuals, including cancer patients. A quantitative microbial risk assessment of Chinese data evaluated the risks of Cryptosporidium and Giardia exposure arising from the application of surface water and septic tank effluent to agricultural land. Exposure via agricultural produce consumption (consumers) and agricultural practices (farmers) was considered for subpopulations of cancer patients and immunocompetent people in urban and rural areas, and risk mitigation scenarios were modelled. The cumulative disease burdens attributable to cryptosporidiosis and giardiasis were, respectively, 9.68×10-6 and 5.57×10-5 disability-adjusted life years per person per year (DALYs pppy) for immunocompetent people, and 3.14×10-5 and 1.51×10-4 DALYs pppy for cancer patients. Cancer patients were approximately three times more likely to have an individual disease burden than immunocompetent people. The disease burden was higher for consumers than farmers, and higher in rural areas than urban areas (all exceeding the maximum recommended by the World Health Organization). The highest burdens were in provinces of high population, such as Henan, Guangdong, and Sichuan, while the burden associated with human and livestock fecal effluent application was higher than with surface water irrigation. Of the three vegetables studied, lettuce posed the greatest risk, followed by bok choy, while cucumber posed the least risk. Risk mitigation scenario analysis showed that pre-treatment of surface water and feces, and appropriate post-harvest handling of vegetables, including disinfection, cooking, and adequate surface heat treatment (75 °C for 60 s), should be considered when attempting to reduce disease burdens. The methodology and findings of this study are useful for evaluating and reducing the burden of Cryptosporidium and Giardia infections associated with agricultural irrigation and fertilization practices, particularly on cancer patients.

Graph Neural Networks With Multiple Prior Knowledge for Multi-Omics Data Analysis.

With the development of biotechnology, a large amount of multi-omics data have been collected for precision medicine. There exists multiple graph-based prior biological knowledge about omics data, such as gene-gene interaction networks. Recently, there has been an increasing interest in introducing graph neural networks (GNNs) into multi-omics learning. However, existing methods have not fully exploited these graphical priors since none have been able to integrate knowledge from multiple sources simultaneously. To solve this problem, we propose a multi-omics data analysis framework by incorporating multiple prior knowledge into graph neural network (MPK-GNN). To the best of our knowledge, this is the first attempt to introduce multiple prior graphs into multi-omics data analysis. Specifically, the proposed method contains four parts: (1) a feature-level learning module to aggregate information from prior graphs; (2) a projection module to maximize the agreement among prior networks by optimizing a contrastive loss; (3) a sample-level module to learn a global representation from input multi-omics features; (4) a task-specific module to flexibly extend MPK-GNN for various downstream multi-omics analysis tasks. Finally, we verify the effectiveness of the proposed multi-omics learning algorithm on the cancer molecular subtype classification task. Experimental results show that MPK-GNN outperforms other state-of-the-art algorithms, including multi-view learning methods and multi-omics integrative approaches.

Spatially explicit model of the Cryptosporidium and Giardia disease burden from surface and ground waters in urban and rural areas of the Three Gorges Reservoir watershed in Chongqing, China.

Cryptosporidium and Giardia (major causes of diarrhea) are widely distributed in Chinese source waters and threaten human health. A new spatially explicit GloWPa-TGR-Crypt-Giar C1 model is presented to simultaneously estimate mean monthly (oo)cyst concentrations in surface and ground waters in the Three Gorges Reservoir (TGR) watershed. A quantitative risk assessment of protozoal infections considered different source waters, transmission pathways, regions, susceptible subpopulations, and drinking water treatments. Monthly mean Cryptosporidium oocyst and Giardia cyst concentrations ranged between 0.5-19.3 oocysts/10 L and 0.2-5.0 cysts/10 L in surface water, respectively, and 0.007-0.3 oocysts/10 L and 0.002-0. 2 cysts/10 L in groundwater. The cumulative disease burdens attributable to cryptosporidiosis and giardiasis were, respectively, 5.77×10-5 DALYs (disability-adjusted life years/person/year) and 4.63×10-6 DALYs in urban areas, and 6.35×10-4 DALYs and 8.84×10-5 DALYs in rural areas, which were much higher than the reference risk level recommended by the World Health Organization ([Formula: see text] DALYs). The annual burden associated with consuming surface water was calculated to be 3.84×10-4 DALYs for Cryptosporidium and [Formula: see text] DALYs for Giardia, whereas consuming groundwater entailed the lower burdens (1.26×10-5 and 3.50×10-6 DALYs, respectively). Most DALYs were a consequence of consumption of directly supplied surface water. Fifty percent of the health burden was carried by immunodeficiency with HIV. Children (0-4 years) were more likely to have an individual disease burden than adults (15-64 years). Males were more susceptible than females. Improving sanitation through adequate ozone and microfiltration treatment should be considered when attempting to reduce disease burden. Sensitivity analysis highlighted the importance of reducing (oo)cyst loads to protect the watershed. The methodology and results described will help in evaluating and reducing the burden of protozoal infection associated with surface and ground waters in the TGR and similar watersheds.